This disclosure relates generally to imaging methods and systems, and more particularly to medical diagnostic imaging methods and systems that acquire and process tissue information for measuring the visceral adipose tissue (VAT) of a subject.
Characteristics of a subject, such as body weight, fat mass, height, girth, gender, age, etc. are clinical descriptors useful by physicians to predict certain health risks that may increase or decrease mortality and morbidity risk. For example, the amount or type of abdominal fat, such as subcutaneous adipose tissue (SAT) or subcutaneous fat and VAT or visceral fat are associated with, and useful predictors of, an adverse metabolic risk profile and certain diseases, such as coronary heart disease, diabetes and stroke. In addition, measuring visceral fat, for example, can relate to metabolic syndrome (i.e., a combination of medical problems that can increase the risk of heart disease, diabetes and/or stroke). People suffering from metabolic syndrome can have some or all of the following: high blood glucose, high blood pressure, abdominal obesity, low high-density lipoprotein (HDL) cholesterol, high low-density lipoprotein (LDL) cholesterol, high total cholesterol and/or high triglycerides.
Conventional methods and systems for measuring VAT are mostly performed using anthropomorphic gauges, bioimpedance gauges, weight scales, etc. These devices often are not capable of providing accurate measurements of VAT because the actual fat content is not being measured, certain assumptions and/or estimates are made during the calculation process, and/or the devices are not exactly calibrated. Also, reproducibility may be difficult, leading to inaccurate comparisons between examinations.
Medical diagnostic imaging systems, such as computed tomography (CT) imaging systems or magnetic resonance (MR) imaging systems have also been used to measure VAT content. However, the use of these systems is often very costly and can expose a subject to high levels of ionizing radiation, for example, when using a CT imaging system. Additionally, these imaging systems are not always available for clinical use and may have long scan times. Moreover, certain measurements are inaccurate in larger subjects.
More sophisticated methods and systems for determining VAT often use simple models to approximate the abdominal volume of a subject from an estimate of subcutaneous fat thickness measurements. However, these methods and systems often fail to accurately estimate SAT, thereby resulting in an inaccurate estimate of VAT. For example, a normal dual-energy X-ray absorptiometry (DXA) image of the abdomen is a planar two-dimensional (2D) image that cannot explicitly measure VAT because it cannot measure the thickness of SAT in the vertical plane. It has been very difficult to determine the thickness of the subcutaneous fat region around the abdomen, especially near the buttocks, since the models used in the past do not take into account differences in the thickness of the subcutaneous fat region around the abdomen near the buttocks.
VAT mass and/or volume numbers are estimated by DXA imaging systems over a sub-volume of the abdomen, such as an android region. The height of this sub-volume is likely to vary according to several factors, primarily scan height and subject height. It may be desirable to compare DXA-based VAT volume or VAT mass estimators to CT or MR based VAT estimators. However, these modalities commonly measure VAT in a single transverse slice with 1 to 5 mm thick slices. Normalized metrics provide the opportunity of lower dose modalities and scan height independence.
Current 2D projection imaging systems such as DXA produce single slice images. Other three-dimensional (3D) imaging modalities such as computed tomography (CT), magnetic resonance (MR), nuclear, positron emission tomography (PET) and ultrasound produce volumetric images. It would be desirable to normalize out the height of the scan region.
Therefore, there is a need for normalized metrics for VAT mass and volume estimations that are independent of scan height and can be used to compare with other normalized metrics across populations.